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Background Healthcare-associated Clostridioides difficile infection (C diff infection, or CDI) imposes a substantial burden on the healthcare system. The impact of an individual C diff infection on onward transmission is not well understood. We developed a model of incident infections using self-exciting stochastic processes, known as Hawkes processes. These models can be used to improve our understanding of the factors that affect the likelihood of new infections to result in additional infections. Methods All patients admitted to a large urban hospital between January 2013 and June 2014 were included. We used Hawkes processes to model the influence of each new CDI case (index infection) on transmission to other patients resulting in additional CDI. We developed separate Hawkes processes for each unit in the hospital to understand the differential impact of a C diff case across units. Units included both semi- and private-room wards, intensive care units, an emergency department, and specialty units such as oncology. Results The magnitude of influence of an index infection on additional infections in the 2 days prior to a C diff test being sent varied across units. Results for an oncology unit, the emergency department, and an all private-room unit are provided (Table 1). An index infection in the emergency department demonstrated the greatest influence, leading to the largest number of additional infections, and increasing in the days leading up to the C diff test being sent. The impact 2 days prior to sample collection was similar across all unit types, and remained constant for oncology unit patients. Conclusion We used Hawkes processes to model the impact of an index C diff infection on onward transmission. We identified differential impacts associated with the unit where the index patient was located in the days leading up to diagnosis. These differences, which could relate to unit-specific factors such as cleaning practices, patient turnover rates, use of portable medical equipment, antibiotic use, and other factors that vary across units, suggest that interventions aimed at controlling CDI may need to consider unit-specific approaches. Disclosures All authors: No reported disclosures.

2387. Learning the Influence of Individual Clostridioides difficile Infections